Abstract: [Objective] In order to satisfy the surface defect recognition quickly with high accuracy, a classification model based on random forest(RF) algorithm is proposed in this study, which can be used for identifying the big shavings, glue spots, debris, oil pollution quickly and accurately on the surface of fibreboard.[Method] Obtaining 100 surface defect images of 4 800 mm×2 400 mm fibreboard, using the Otsu algorithm to realize image segmentation, features including area(S), length(L), the length-width tatio(OR), compactness(J), rectanglarity(P), circularity(O), mean value(u), standard deviation(σ_D), smoothness(σ_P),skewness(σ_S), kurtosis(σ_K) and root mean square value(σ_R) of defect area were extracted, these were used as the experimental data. The experimental data were used for constructing RF classifier, choosing 2/3 data and eight features randomly by bootstrap method to construct k decision trees, the RF classifier were consist of these trees. The number of k is determined by calculating the outside data of the bag(OOB) error rate. 100 fibreboards with the wood shavings, glue spots, debris, and oil pollution were used to test by RF classifier.[Result] While k=600, the lowest average OOB error rate of the RF classifier is 0.004, the recognition accuracy of test is 99%, and the recognition time of one fibreboard is 525 ms. Therefore, the RF classifier is superior to NN and SVM on recognition time and accuracy.[Conclusion] The study proves the classifier based on RF algorithm is more feasibility and superiority on defect identification of fibreboard surface, it can achieve surface defect recognition quickly with high accuracy, satisfy the needs of on-line defect detection system of fibreboard.

Key words: fibreboard, defect recognition, classification model, feature extraction, random forest (RF)